@inproceedings{li-zhou-2022-sapphire,
    title = "Sapphire at {S}em{E}val-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks",
    author = "Li, Sihui  and
      Zhou, Xiaobing",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.semeval-1.54/",
    doi = "10.18653/v1/2022.semeval-1.54",
    pages = "405--408",
    abstract = "This paper introduces the related work and the results of Team Sapphire`s system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection. We only participated in subtask 1. The task goal is to judge whether a news text contains PCL. This task can be considered as a task of binary classification of news texts. In this binary classification task, the BERT-base model is adopted as the pre-trained model used to represent textual information in vector form and encode it. Capsule networks is adopted to extract features from the encoded vectors. The official evaluation metric for subtask 1 is the F1 score over the positive class. Finally, our system`s submitted prediction results on test set achieved the score of 0.5187."
}